Perception study of state support programs for companies, in Talca, Chile in times of Covid 19

This work identifies the factors that influence the perception of company managers regarding the state support programs carried out in times of Covid19. A questionnaire was applied to a sample of company executives from the city of Talca, Chile. Descriptive, exploratory factor analysis and structural modeling ratified by the relevant goodness of fit indices were carried out. The results confirm the existence of three factors that affect the perception of managers that include 12 significant items. It is concluded that the investment factor acts as an independent dimension in the model, the classification factor of the companies acts as a mediator and finally the competitiveness factor turns out to be the dependent dimension of the model.


Introducció n
The coronavirus, as a global threat, has been spreading for two decades, first as a respiratory syndrome and now as SARCOV-2, from a probable zoonotic source, spread by direct contact. However, confirmed diagnosis by reverse transcriptase PCR makes individual management possible by applying supportive therapy and mechanical ventilation in severe cases, in addition to reducing community spread through isolation [1]. Consequently, from the governmental perspective, two analysis options are promoted, one with a citizen focus and the other oriented to organizations to achieve subsistence [2].
In times of Covid19, governments have faced the challenge of helping people and organizations in general [3]. At the citizen level, with the help of local organizations that educate about vaccination and implement safe immunological programs [4] and at the organizational level, via business continuity plans and crisis management plans, without discriminating against these companies or institutions through the relevant legal mechanisms and in contact with corporate governments [5]. Thus, for example, at the individual level, relative success was proportion of these fail to remain in the market for more than 5 years. Micro, Small and Medium Enterprises (MSMEs) are added along this same line, whose innovation capabilities are limited to the incorporation of information and communication technologies (ICT), since in the short term they have facilitated their processes and generated some growth spaces for its sales and greater profitability [21].

Government aid
In the opinion of Antunes, et al. [22] governments are obliged to execute rescue plans for companies, precisely because their timely intervention [2] would allow sustaining the production, financing and stability of the companies. jobs in times of turbulence. Thus, for example, at the level of the general population, people with economic problems depend on health benefits from State institutions, from those who receive medical care and regular check-ups, which, however, require long waiting periods to generalize their beneficial effects, which is why, at the international level, a solid strategy requires pandemic management programs at the level of the countries that make up the South American region [23]. As of July 2020, it affected Trinidad and Tobago, which has a Human Development Index (HDI) of 0.79 and which positions it as the richest Caribbean country in the area, on the contrary, the North Caribbean Island, Haiti, received the impact of COVID-19 in the month of May of the same year, deteriorating its relative position and placing it in position 170 of 189 countries in the world [24]. Similarly, the islands of Jamaica, Aruba, Bahamas and Saint Vincent suffered the effects of COVID-19 also during the year 2020, being monitored and informed weekly to achieve greater effectiveness of their public policy [23]. However, governments seek to sustain their economies [12] through subsidies and transfers [7] as well as perfecting regulations [25]. From this perspective, in Australia, the state intervened by stimulating the development of companies beyond its regulatory role [26], while in Romania, the benefits were delivered under limited conditions and terms [26]. In this context Rincón and Mujica [27], analyzed the impact produced by the Barrio Adentro ll program in the State of Zulia, Venezuela, applying the method of participatory perception seeking to relate problems with social programs, confirming the good reception that free programs have. for the benefit of the community [27]. A second study carried out in the Biobío Region, Chile, analyzed the perception of organizations on the occasion of the 2017 presidential elections, highlighting variables such as political uncertainty and decreased demand, confirming that the priority of organizations is more than anything growth followed by its alignment with the environment [28]. Finally, in Uruguay in 2019, the contingency in the country was studied, determining that Uruguayan firms perceived that economic activity, investment and employment levels would decrease, however, when comparing these perceptions with those obtained in 2018, it was possible appreciate a slight percentage increase in the positive vision of the future by Uruguayan companies [29].

Perception
Perception [30] is the result of a reflection process carried out by a person on the elements that make up the environment, relating stimuli to receptors and which has been ratified by Arias [31] and Seland [30] who refer to the perception to the recognition and interpretation that the individual makes of the information captured through the senses, and that Kumar [32] classifies as diverse and even opposed between different individuals. This is how Arias [31] defines perception as an endogenous human capacity, analogous to each person, confirming the findings of Bustamante-Ubilla et al. [33] when he points out that perception depends on the context that surrounds the individual and the interpretation let him make his reality. In short, perception collaborates in the construction of mental models or schemes [34] that allow the person and the entities they lead to know, understand and assume a certain reality in order to act in it in a coherent and effective manner [35].
Based on the above, the present work identifies the latent factors, the relationships and incidences that determine the perception components of the state support programs in times of Covid-19 of the managers of the companies in Talca, Chile.

Methodology
This work was carried out by applying a cross-sectional and correlational study of the data [36]. First, a descriptive analysis of the sample of managers interviewed on behalf of their respective companies was carried out. Secondly, a first inferential analysis was carried out aimed at achieving the focus of the dimensions under analysis and which was carried out by applying Exploratory Factor Analysis, AFE [37]. Finally, progress was made towards the relationship and causality analyzes of the key dimensions by applying Exploratory Structural Equation Modeling, ESEM, as a semi-confirmatory alternative [38] and which were carried out by applying analysis of covariance and variance, respectively. Finally, the data processing was carried out using the SPSS V23 software, for the descriptive analyzes [36], additionally the Factor.10.4. win64 program was used for the exploratory analysis, AFE [39] and finally for the ESEM analysis, the IBM-SPSS-Amos program was used.
To carry out this investigation, the data sources of the Department of Economic and Tax Studies of the Subdirectorate of Strategic Management and Tax Studies of the Internal Revenue Service, SII [40], were available as of July 2019 and the data from active commercial patents during the first quarter of 2020, issued by the Headquarters of the Transparency Unit of the Illustrious Municipality of Talca [41], dated April 4, 2020. From these two sources of data, a random search of cases was carried out in order to organize the interviews necessary for the study.
The database was organized in the Excel program, listing each of the available contacts after verifying the completeness of the records, proceeding to eliminate duplicate records. Subsequently, the random case search option was used to identify the organizations to be interviewed. Finally, a random list of companies to contact was obtained.

Population and sample
The sample unit of the investigation was the companies selected according to the inclusion criteria. As a first criterion, the study focused on those located in the city of Talca, Maule Region of Chile, formally represented by their respective directors. The second inclusion criterion was that they were formally active companies in the official records of the State, managing to identify a population of companies in the city of Talca that totals 1,375 organizations from various sectors and sizes, forming a finite universe. Naturally, as an exclusion criterion, the other companies in the region located in three cities of the region (Curicó, Linares and Cauquenes) were not included in the universe of studies. For the determination of the sample a priori, an error of 5% was foreseen, a maximum variance of 0.25, corresponding to a probability of success and failure of 50% (p = 0.5 � q = 0.5) and a confidence level of 95%, reaching a total of 301 cases.

Interviews
Given the context of Covid 19, it was planned to conduct the interviews [42] p.217 in this case, through the application of an online questionnaire, feasible to be implemented through the Google form website, to be sent electronically. to the contacts of the previously determined sample, incorporating a direct access link to the digital questionnaire.
The questionnaire was addressed to the directors of the companies, who are assumed to have the necessary knowledge of the activities of their respective organizations as well as the information on the necessary government aid that allows them to face the particular environments of each of the companies interviewed. Based on these minimum criteria, valid and reliable information could be collected for the study.

Instrument description
After a previous study of variables related to state aid available in the Chilean context, it was possible to create an initial list of 57 items, which were analyzed and regrouped by similarity, creating a refined inventory of questions to generate a questionnaire. He foresaw the consultation of a group of 7 experts, university professors and business managers, who were presented with an initial questionnaire [42] p.217 to collect their observations regarding the questions asked regarding state support programs in the contingency context caused by the corona virus pandemic . Once the observations were received, the duly revised instrument was structured into three sections containing the questions defined and approved to be presented to the interviewees in correlative order and associated with a five-point categorical scale [43].
Once the design of the questionnaire was completed, the instrument was revised and adjusted, verifying its text and its corresponding adaptation to the context of application [44]. Content and structure were verified by a panel of experts who evaluated the texts of each question, verifying their syntax, wording and unidirectionality of the statements in order to achieve an instrument that was easy to read and understand by the interviewees. In addition, a field validation was carried out through the application of pilot tests aimed at finding expression, writing and syntax errors from the perspective of the interviewees, to ensure the due understanding of the interviewees to the proposed items. An adequate selection of the subjects planned for the study was taken into consideration, however, ensuring that these contacts were not incorporated into the final study sample. Finally, the correction of the writing and coherence of expression and writing that were pertinent to incorporate were made. The pilot test contacted was 10% of the planned sample in order to verify the operability of the instrument [33].
The Project was approved by the ESPOL University Research Dean with Code FCSH-14-2021 and the consent of the participants was informed in writing at the beginning of the questionnaire where the participants agreed to participate in the study.
The instrument contains an introduction in which the objective of the study is disclosed. In the first part, the questionnaire collects the characteristics that allow defining the profile of the entities. In the second part, a list of statements is presented that refer to government programs of state aid to organizations in Chile, especially relevant in times of Covid 19. In the third part, a series of statements are presented regarding general aspects related to the mechanisms for assigning, distributing and obtaining resources foreseen in government programs of government support. Finally, both in the second and in the third part of the instrument, the responses of the interviewees are collected using a discrete Likert scale of 1:5 points [39].

Factor analysis
An exploratory factorial analysis of the observable items was performed [45] seeking to reduce the observations to a limited number of latent dimensions that synthesize the instrument [39]. For this, the data adequacy index was verified by calculating the minimum Kaiser-Meyer-Olkin coefficient (KMO) (� 0.6) and a significant Bartlett sphericity test ( ��� ) [46], which allows verifying that the correlation matrix is different from the identity matrix, with correlation coefficients other than 0 and looking for the value (p) to be less than the expected significance value (� 0.05) [47].
The extraction and rotation of factors [45] took into account the respective eigenvalue corresponding to the measure of variability of the amount of information that can explain a factor, assigning an initial value equal to 1, which allows to explain most of the total variability of the observable items under study [48] and, for the conformation of the factors, it was decided to require high communality indexes (� 0.3) from the reagents and factor loadings of the reactive with the factor also high (� 0.5) [45].

Structural modeling
The approach of the validation model [49] was formulated with all the reagents using the Method of Structural Equations (Exploratory Structural Equation Modeling) ESEM, as a semiconfirmatory alternative, [50]. In addition, given the response options of the questionnaire on a Likert scale 1:5 points that allow collecting frequencies that behave in an approximately normal way [46], it was possible to assume that it was possible to overcome the risks that could lead to non-linear relationships. Therefore, we worked with the observable variables or instrument items defining them as polychoric [39], which were characterized as endogenous, mediating or exogenous latent variables to design the structural equation model [50].
The previous steps of the confirmatory analysis suggested by Kaplan [48] were taken into consideration and the structural analysis was carried out to ratify the latent variables of the model [50,51], opting for sensitize standardized values and significance indices of the estimators [38]. To ratify the findings, the Comparative Fit Index (CFI), Goodness of Fit Index (GFI) and Adjusted Goodness of Fit Index (AGFI) were observed [50], verifying that their values are between values 0 and 1, where 1 indicates a perfect fit.

Use of software
The analysis and processing of the data was carried out using the statistical software SPSS V23 [36]. For modeling using structural equations, AMOS from [39,51] was used, applying the Maximum Likelihood method, ML. Furthermore, in order to reduce the effects of variance [53], standardized estimators were interpreted.

Resultados
The results of the descriptive, factorial and structural equation modeling analyzes are presented below.

Descriptive analysis
The real sample of interviewees reached 313 managers of companies in the city of Talca, which allowed reducing the margin of error of the study from 5% to 4.88%.
Observing Table 1, 54.3% of the sample corresponds to small companies; while 30% corresponds to medium-sized companies. 31.3% of the companies in the city of Talca have been in the market for 9 years or less. 75.4% of the sample has been a beneficiary of state programs. These benefits have been granted more frequently by state entities such as the Technical Cooperation Service, Sercotec (15.7%), Production Development Corporation, Corfo (14.1%) and ProChile (10.5%). It can also be seen that the perception of the general aspects of government support programs are answered in the categories of agreement and totally agree, on the Likert scale equivalent to the higher frequencies (4 and 5). Regarding, if the seniority of a company determines whether or not it accesses benefits, the option of agreement adds 30.7% to which the totally agreement alternative is added, which reaches 26.6%, totaling 57.3%. In relation to whether the benefits are currently accessible to all types of companies, the totally agree option reaches 34.5%, while the agree alternative amounts to 28.1%, adding 61.6%.
On the other hand, when asked if the benefits should be directed to regions with high unemployment rates, the interviewees indicated that they agreed with a relative frequency of 32.3%, followed by those who totally agreed that they reach 31.9%, reaching the sum of 64.2%. In addition, regarding whether the requirements for benefits are reduced, the application for them would increase, the interviewees who answered the category of agreement marked 26.5% and those who registered the option totally agreed reached 72.2%, adding 98, 7%. Finally, regarding whether it is necessary to improve the promotion that the benefits currently have, the responses again focus on the superior response options. In agreement they marked 33.9% and totally in agreement they registered 47.9% of the interviewees, totaling 81.8%.

Factor analysis
The factorial analysis of principal components considering extraction by the varimax method, managed to determine a KMO index of 0.793, a significant Bartlett Sphericity test (0.000) and a significant Cronbach α reliability index of 0.752 ( ��� ) for the instrument in its entirety ( Table 2).
The factorial allowed to determine three factors that group 12 reagents with their corresponding factorial loads and communality indices. In general, the factors confirm that all the items reach higher communality indices (� 0.30) and that their factor loads with their respective factors are also high (� 0.50) ( Table 2).
Based on the factors detailed in Table 2, the first factor called F1: Investment, made up of 6 items (V17, V18, V19, V20, V21 and V24) achieves an explained variance of 25.439% and a In a first interpretation, it can be inferred that the investment factor (F1) and its respective reagents refer to the importance for managers of the investment that can be made within the organization with the benefits obtained. According to this, this investment factor F1 is directly related to the allocation of the benefits received, which are used within the company in infrastructure, processes, products; as well as they are destined towards human resources, in the person of the employees, for example, investing in remuneration and better working conditions.
The factor called classification of companies (F2) is linked to aspects related to obtaining state benefits, of course according to the type of company. This factor corresponds to the scenario in which companies currently find themselves; It also refers to the type of company and markets to which they belong and, of course, in relation to the financial situation that distinguishes between healthy companies or those that have faced some type of failure, emphasizing the importance of classifying a company in any of these categories at the time of applying for state benefits, to be effectively beneficiaries of these. Finally, the factor (F3) related to market competitiveness is related to the competitiveness variables, narrowing the relationship between obtaining state benefits and the increase in the company's competitiveness in the market. From this perspective, this last factor refers to the increase in competitiveness by means of obtaining benefits, which will allow companies to establish themselves in the market and, at the same time, increase the entry barriers for new companies that intend to enter the market. the domain space of each of them in the industry.

Structural modeling
The results obtained from the structural modeling analyzes using the covariance and variance methods to determine the relationships and incidences that occur between the independent, mediating and dependent factors under study are described below.

Análisis de covarianza.
Through the covariance analysis and applying the maximum likelihood method, ML, it was possible to identify the relationships that occur between the determined factors and that are presented in detail in Table 3.
The relationships of the 3 extracted factors are appreciated. The covariance between the company classification factor (F2) and the investment factor (F1) is 9.2%, between the company classification factor (F2) and the market competitiveness factor (F3) is 21.18%. and, between the market competitiveness factor (F3) and the investment factor (F1) it is 45.6%, which demonstrates the potential of these relationships.
The goodness-of-fit indices of the model are confirmatory. The CMIN/DF index reaches the value 1.632 (� 3), CFI equal to 0.959 (� 0.90) and an RMSEA error index equal to 0.045 (� 0.05), which ratifies a correct adjustment of the data confirming the relationship model obtained through the covariances, so that it effectively allows us to assume that the three factors of the model are related in a pertinent way.

Análisis de varianza.
The analysis of variance allowed determining the incidence relations between the determined factors. In general, it was possible to determine the respective rates of effect of one factor on another, determining a dependent factor, a second mediator and an independent factor. Table 4 shows the cause-effect relationship between the factors.
It is shown that the investment factor (F1) influences the company classification factor (F2) by 8.4%, while the company classification factor (F2) influences the market competitiveness factor (F3) by 18. two%; On the other hand, the investment factor (F1) has a 43.2% influence on the market competitiveness factor (F3). Checking these incidence relationships, the adjustment coefficients of the structural model present conforming results, starting with the CMIN/ DF index equal to 1.539 (� 3) which ratifies the model adjustment, an RMSEA error index equal to 0.042 less than the maximum required (� 0.05) with which it confirms the approximation of the model with reality and a CFI index of 0.966 (� 0.9) which indicates that the model is acceptable.
In addition, Fig 1 shows the modeling of the factors according to the analysis of covariances [51,53] that establishes the mutual relationships between the factors and also shows the model determined by the variance method that establishes the causalities between the factors [39,50].
According to this modeling that determines incidences, the investment factor (F1) acts as the independent factor, the market competitiveness factor (F3) as the dependent factor of the model. Finally, the company classification factor F2 assumes a mediating role in the modeling.

Discusió n
Starting the analysis, it is appreciated, in the first place, the relevance that the management and handling, by state agencies, of the pandemic from its origin and its progressive evolution to the present had and has to date.
In this regard, the directors interviewed, just like each of the Chileans, were subject to the plans that the State in general established for the management of COVID-19. Thus, for example, the vaccination status of the study sample evolved progressively according to the implemented plans [54]. In this context, as of December 2019, the Epidemiology Department of the Ministry of Health created the Epivigila computer platform (p.28) associated with a Preparedness and Response Plan for epidemiological surveillance, diagnostic reinforcement, and risk  effort is valued, which, by mid-2021, has 17,200 new permanent health professionals and 32,000 additional personnel to meet specific needs for critical periods [54]. Finally, by the end of 2021, 20% of the population would have been rehabilitated, whose projection by 2022 reaches 40% of the target population, and by 2023 it is expected that therapies would be delivered to 40% of the remaining population [54] p.137. From the technical perspective of the analysis, the present job contributes to the knowledge of the factors that influence the perception of company managers regarding state benefits. Relationships can be established between the items contained in the extracted dimensions and the variables mentioned in the theoretical framework regarding State intervention.
Regarding the investment factor (F1), company directors consider investing the funds received in the company's production processes, in addition to designing and improving the products and services offered, which according to Benedettini et al. [55] contributes to the decrease in the probability of bankruptcy.
Regarding the factor called classification of companies (F2), the directors of the firms agree with the segmentation according to the size of the company, as indicated by Crespi [56], Lopucki & Doherty [57]. Like Barboza et al. [58], the directors of the companies consider that the delivery of benefits should vary according to the current financial situation of the organization. As mentioned by Alaminos et al. [59] and Arroyave [60] the financial situation will be crucial to predict the future scenario of the firm.
Continuing with the market competitiveness factor (F3), the directors of the organizations agree with the relationship between obtaining benefits and increasing market competitiveness, a situation that, according to Heredia & Sánchez [2], makes it timely to intervention of the State towards the companies. Consequently, given that in conditions of pandemic restriction the feasibility of competing in the market is restricted and barriers are increased, adjustment processes are stimulated, as pointed out by Yoguel et al. [61], precisely to be able to adapt to changes.

Conclusiones
Regarding the objectives set and the results obtained, it is concluded that, in general, the variables that make State intervention in companies opportune in pandemic conditions turn out to be market competitiveness, financial management of organizations, level of indebtedness, financing barriers, productivity and levels of innovation.
It is ratified that the directors of the Talca firms recognize the importance of investing the benefits received within the organization to generate improvements, considering the segmentation of the firms to apply for and obtain state benefits relevant. Regarding market competitiveness, they identify the relevance of obtaining benefits for the development of a firm, which brings with it an increase in market competitiveness and the raising of barriers. Consequently, the dimensions that bring together the key components of the perception of the managers of the companies in Talca regarding the state benefits that they can access in times of Covid-19 are synthesized three factors of investment, classification of companies and market competitiveness.
Finally, among the three key factors identified, relevant relationships and incidences are established, concluding that the investment factor acts as an exogenous and independent factor, the company classification factor assumes a mediating role in the modeling and the market competitiveness factor, It turns out to be the endogenous or dependent factor within the structural model that determines the perceptions of the managers of companies in Talca, regarding state support programs in times of Covid-19.

Limitation
Although the present work managed to identify a population of 1,375 managers gradually accessible due to their location in the city of Talca, it would have been interesting to have been able to access a broader sample of the other cities of the Maule Region, such as those located in Curicó, Linares. and Cauquenes, however, a significant sample of 313 executives representative of the companies in the city of Talca was achieved, reducing the margin of error of the analyzes carried out.
As a result of the above, it is estimated that the steps of analysis, follow-up and subsequent additional studies that are suggested and that derive from the work carried out, are proposed to address the issue from three complementary dimensions. First, carry out a second study in the same city of Talca once the end of the pandemic is declared and the adverse effects of COVID-19 have been reduced. Second, to carry out a broader study of regional coverage that incorporates, on this occasion, the other cities of the Maule region of Chile, in order to obtain a broader analysis that allows ratifying the findings reached in this first work. Finally, it is proposed to validate a research instrument, in the form of a psychometrically structured questionnaire, that allows the future study of state support for organizations in times of social impact.